Cox proportional hazards model is used to determine significant predictors for outcomes that are time-to-event. It is especially relevant in disciplines such as oncology, where outcomes are usually time-to-event (e.g overall survival and disease-free survival). Due to the complex nature of time-to-event outcome which involves censoring as well as both continuous and categorical components, it may be difficult to understand how to interpret the model initially. Hence, similar to what I did in my previous article on logistic regression, I would examine how to interpret R outputs for cox proportional hazards model as well as a test for proportional hazard assumption in the model.
Glossary for statistical terms for the later section:
For the dataset, I will be using the colon dataset from the _survival _package. The data was collected from a clinical trial, which tested on the use of adjuvant chemotherapy regimens (Levamisole and Levamisole + 5-FU) for patients with colon cancer. While there are several variables in the dataset, we will be focusing on these variables to build the Cox proportional hazard model:
#r-programming #regression #survival-analysis #rstudio #data analysis